
import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase    :   int						=			random.Random()

if is_torch_available():
		import torch




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Any		,    _lowerCamelCase					:     Tuple=1.0		,    _lowerCamelCase					:     int=None		,    _lowerCamelCase					:     Optional[Any]=None)   ->       Optional[Any]:






							'''simple docstring'''





							if rng is None:
														__UpperCamelCase      :  Any       =   global_rng

							__UpperCamelCase      :  int       =   []
							for batch_idx in range(shape[0]):
														values.append([])
														for _ in range(shape[1]):
																					values[-1].append(rng.random() * scale)

							return values




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:Union[str, Any]	,  a						:Optional[int]	,  a						:List[str]=7	,  a						:Union[str, Any]=4_0_0	,  a						:List[Any]=2_0_0_0	,  a						:Any=1	,  a						:int=0.0	,  a						:Union[str, Any]=1_6_0_0_0	,  a						:Optional[int]=True	,  a						:Union[str, Any]=True	,  )						->					int:
								__UpperCamelCase      :  Optional[int]       =   parent
								__UpperCamelCase      :  Any       =   batch_size
								__UpperCamelCase      :  int       =   min_seq_length
								__UpperCamelCase      :  Optional[Any]       =   max_seq_length
								__UpperCamelCase      :  str       =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__UpperCamelCase      :  str       =   feature_size
								__UpperCamelCase      :  Any       =   padding_value
								__UpperCamelCase      :  Tuple       =   sampling_rate
								__UpperCamelCase      :  List[str]       =   return_attention_mask
								__UpperCamelCase      :  Tuple       =   do_normalize







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Any:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "return_attention_mask": self.return_attention_mask,
								    "do_normalize": self.do_normalize,
								}







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:Any=False	,  a						:List[str]=False		)						->					List[Any]:
								def _flatten(a						:List[str]		):
															return list(itertools.chain(*a		)		)

								if equal_length:
															__UpperCamelCase      :  Tuple       =   floats_list((self.batch_size, self.max_seq_length)		)
								else:
															# make sure that inputs increase in size
															__UpperCamelCase      :  Tuple       =   [
															    _flatten(floats_list((x, self.feature_size)		)		)
															    for x in range(self.min_seq_length	,  self.max_seq_length	,  self.seq_length_diff		)
															]

								if numpify:
															__UpperCamelCase      :  Dict       =   [np.asarray(a		) for x in speech_inputs]

								return speech_inputs




@require_torch
@require_torchaudio
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	ASTFeatureExtractor







	def   _lowerCamelCase							(   self						:Dict		)						->					Any:
								__UpperCamelCase      :  List[str]       =   ASTFeatureExtractionTester(self		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__UpperCamelCase      :  Optional[Any]       =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								# create three inputs of length 800, 1000, and 1200
								__UpperCamelCase      :  Optional[int]       =   [floats_list((1, x)		)[0] for x in range(8_0_0	,  1_4_0_0	,  2_0_0		)]
								__UpperCamelCase      :  Optional[Any]       =   [np.asarray(a		) for speech_input in speech_inputs]

								# Test not batched input
								__UpperCamelCase      :  Union[str, Any]       =   feat_extract(speech_inputs[0]	,  return_tensors="np"		).input_values
								__UpperCamelCase      :  Dict       =   feat_extract(np_speech_inputs[0]	,  return_tensors="np"		).input_values
								self.assertTrue(np.allclose(a	,  a	,  atol=1E-3		)		)

								# Test batched
								__UpperCamelCase      :  List[str]       =   feat_extract(a	,  padding=a	,  return_tensors="np"		).input_values
								__UpperCamelCase      :  str       =   feat_extract(a	,  padding=a	,  return_tensors="np"		).input_values
								for enc_seq_a, enc_seq_a in zip(a	,  a		):
															self.assertTrue(np.allclose(a	,  a	,  atol=1E-3		)		)

								# Test 2-D numpy arrays are batched.
								__UpperCamelCase      :  Optional[int]       =   [floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__UpperCamelCase      :  Optional[Any]       =   np.asarray(a		)
								__UpperCamelCase      :  Tuple       =   feat_extract(a	,  return_tensors="np"		).input_values
								__UpperCamelCase      :  List[str]       =   feat_extract(a	,  return_tensors="np"		).input_values
								for enc_seq_a, enc_seq_a in zip(a	,  a		):
															self.assertTrue(np.allclose(a	,  a	,  atol=1E-3		)		)







	@require_torch
	def   _lowerCamelCase							(   self						:Dict		)						->					Union[str, Any]:
								import torch

								__UpperCamelCase      :  List[str]       =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								__UpperCamelCase      :  Optional[Any]       =   np.random.rand(1_0_0		).astype(np.floataa		)
								__UpperCamelCase      :  str       =   np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
															__UpperCamelCase      :  Optional[Any]       =   feature_extractor.pad([{"input_values": inputs}]	,  return_tensors="np"		)
															self.assertTrue(np_processed.input_values.dtype == np.floataa		)
															__UpperCamelCase      :  Dict       =   feature_extractor.pad([{"input_values": inputs}]	,  return_tensors="pt"		)
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:Optional[int]		)						->					Dict:
								from datasets import load_dataset

								__UpperCamelCase      :  Any       =   load_dataset("hf-internal-testing/librispeech_asr_dummy"	,  "clean"	,  split="validation"		)
								# automatic decoding with librispeech
								__UpperCamelCase      :  Tuple       =   ds.sort("id"		).select(range(a		)		)[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]







	@require_torch
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Tuple:
								# fmt: off
								__UpperCamelCase      :  Optional[int]       =   torch.tensor(
								    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
								     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
								     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
								     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]		)
								# fmt: on

								__UpperCamelCase      :  List[Any]       =   self._load_datasamples(1		)
								__UpperCamelCase      :  Any       =   ASTFeatureExtractor()
								__UpperCamelCase      :  Any       =   feature_extractor(a	,  return_tensors="pt"		).input_values
								self.assertEquals(input_values.shape	,  (1, 1_0_2_4, 1_2_8)		)
								self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,  a	,  atol=1E-4		)		)

from __future__ import annotations

lowercase    :   Dict						=			tuple[int, int, int]
lowercase    :   List[str]						=			tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowercase    :   int						=			'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
lowercase    :   Optional[int]						=			'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowercase    :   Optional[Any]						=			'FOBHMDKEXQNRAULPGSJVTYICZW'
lowercase    :   str						=			'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowercase    :   Tuple						=			{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
lowercase    :   Optional[int]						=			'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowercase    :   List[Any]						=			'SGLCPQWZHKXAREONTFBVIYJUDM'
lowercase    :   Dict						=			'HVSICLTYKQUBXDWAJZOMFGPREN'
lowercase    :   List[Any]						=			'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowercase    :   Optional[Any]						=			'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowercase    :   Tuple						=			'KOAEGVDHXPQZMLFTYWJNBRCIUS'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     RotorPositionT		,    _lowerCamelCase					:     RotorSelectionT		,    _lowerCamelCase					:     str)   ->       tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:






							'''simple docstring'''





							if (unique_rotsel := len(set(_lowerCamelCase))) < 3:
														__UpperCamelCase      :  Tuple       =   F'Please use 3 unique rotors (not {unique_rotsel})'
														raise Exception(_lowerCamelCase)

							# Checks if rotor positions are valid
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  str       =   rotpos
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'First rotor position is not within range of 1..26 ({rotorposa}'
														raise ValueError(_lowerCamelCase)
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  int       =   F'Second rotor position is not within range of 1..26 ({rotorposa})'
														raise ValueError(_lowerCamelCase)
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  List[Any]       =   F'Third rotor position is not within range of 1..26 ({rotorposa})'
														raise ValueError(_lowerCamelCase)

							# Validates string and returns dict
							__UpperCamelCase      :  str       =   _plugboard(_lowerCamelCase)

							return rotpos, rotsel, pbdict




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str)   ->       dict[str, str]:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'Plugboard setting isn\'t type string ({type(_lowerCamelCase)})'
														raise TypeError(_lowerCamelCase)
							elif len(_lowerCamelCase) % 2 != 0:
														__UpperCamelCase      :  int       =   F'Odd number of symbols ({len(_lowerCamelCase)})'
														raise Exception(_lowerCamelCase)
							elif pbstring == "":
														return {}

							pbstring.replace(" "		,    "")

							# Checks if all characters are unique
							__UpperCamelCase      :  Optional[int]       =   set()
							for i in pbstring:
														if i not in abc:
																					__UpperCamelCase      :  Tuple       =   F'\'{i}\' not in list of symbols'
																					raise Exception(_lowerCamelCase)
														elif i in tmppbl:
																					__UpperCamelCase      :  Tuple       =   F'Duplicate symbol ({i})'
																					raise Exception(_lowerCamelCase)
														else:
																					tmppbl.add(_lowerCamelCase)
							del tmppbl

							# Created the dictionary
							__UpperCamelCase      :  Union[str, Any]       =   {}
							for j in range(0		,    len(_lowerCamelCase) - 1		,    2):
														__UpperCamelCase      :  Union[str, Any]       =   pbstring[j + 1]
														__UpperCamelCase      :  str       =   pbstring[j]

							return pb




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     RotorPositionT		,    _lowerCamelCase					:     RotorSelectionT = (rotora, rotora, rotora)		,    _lowerCamelCase					:     str = ""		,    )   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  List[Any]       =   text.upper()
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   _validator(
							    _lowerCamelCase		,    _lowerCamelCase		,    plugb.upper())

							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   rotor_position
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   rotor_selection
							rotorposa -= 1
							rotorposa -= 1
							rotorposa -= 1

							__UpperCamelCase      :  List[str]       =   []

							# encryption/decryption process --------------------------
							for symbol in text:
														if symbol in abc:
																					# 1st plugboard --------------------------
																					if symbol in plugboard:
																												__UpperCamelCase      :  Dict       =   plugboard[symbol]

																					# rotor ra --------------------------
																					__UpperCamelCase      :  Optional[int]       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  List[Any]       =   rotora[index % len(_lowerCamelCase)]

																					# rotor rb --------------------------
																					__UpperCamelCase      :  Dict       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  Any       =   rotora[index % len(_lowerCamelCase)]

																					# rotor rc --------------------------
																					__UpperCamelCase      :  str       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  Union[str, Any]       =   rotora[index % len(_lowerCamelCase)]

																					# reflector --------------------------
																					# this is the reason you don't need another machine to decipher

																					__UpperCamelCase      :  Union[str, Any]       =   reflector[symbol]

																					# 2nd rotors
																					__UpperCamelCase      :  Optional[int]       =   abc[rotora.index(_lowerCamelCase) - rotorposa]
																					__UpperCamelCase      :  Optional[Any]       =   abc[rotora.index(_lowerCamelCase) - rotorposa]
																					__UpperCamelCase      :  str       =   abc[rotora.index(_lowerCamelCase) - rotorposa]

																					# 2nd plugboard
																					if symbol in plugboard:
																												__UpperCamelCase      :  Optional[Any]       =   plugboard[symbol]

																					# moves/resets rotor positions
																					rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  Any       =   0
																												rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  Tuple       =   0
																												rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  List[str]       =   0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
														result.append(_lowerCamelCase)

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Optional[Any]						=			'This is my Python script that emulates the Enigma machine from WWII.'
		lowercase    :   int						=			(1, 1, 1)
		lowercase    :   Optional[Any]						=			'pictures'
		lowercase    :   Optional[Any]						=			(rotora, rotora, rotora)
		lowercase    :   Optional[Any]						=			enigma(message, rotor_pos, rotor_sel, pb)

		print('Encrypted message:', en)
		print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))